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Zhao, Yujie, Zhanyong Tang, Guixin Ye, Xiaoqing Gong, and Dingyi Fang. "Input-Output Example-Guided Data Deobfuscation on Binary." Security and Communication Networks 2021 (December 13, 2021): 1–16. http://dx.doi.org/10.1155/2021/4646048.
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Data obfuscation is usually used by malicious software to avoid detection and reverse analysis. When analyzing the malware, such obfuscations have to be removed to restore the program into an easier understandable form (deobfuscation). The deobfuscation based on program synthesis provides a good solution for treating the target program as a black box. Thus, deobfuscation becomes a problem of finding the shortest instruction sequence to synthesize a program with the same input-output behavior as the target program. Existing work has two limitations: assuming that obfuscated code snippets in the target program are known and using a stochastic search algorithm resulting in low efficiency. In this paper, we propose fine-grained obfuscation detection for locating obfuscated code snippets by machine learning. Besides, we also combine the program synthesis and a heuristic search algorithm of Nested Monte Carlo Search. We have applied a prototype implementation of our ideas to data obfuscation in different tools, including OLLVM and Tigress. Our experimental results suggest that this approach is highly effective in locating and deobfuscating the binaries with data obfuscation, with an accuracy of at least 90.34%. Compared with the state-of-the-art deobfuscation technique, our approach’s efficiency has increased by 75%, with the success rate increasing by 5%.
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Do, Nam H., Tien Van Do, Lóránt Farkas, and Csaba Rotter. "Provisioning Input and Output Data Rates in Data Processing Frameworks." Journal of Grid Computing 18, no. 3 (March 3, 2020): 491–506. http://dx.doi.org/10.1007/s10723-020-09508-0.
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Abstract This paper is motivated by the need of deadline-bounded applications in live mobile network environments to obtain the guarantee and the appropriate share of an input and output (I/O) data rate. However, data processing frameworks only support the request of memory and the computing capacity at present. In this paper, we propose a solution that allows the control of disk I/O and network I/O for data processing applications in YARN and Mesos frameworks. Experimental results show that our tool can provision the I/O data rate sharing of competing data processing applications.
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Chen, Yirun, and Wensheng Dai. "Tracking Control of the Dynamic Input-Output Economic System Based on Data Fusion." Security and Communication Networks 2022 (March 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/1461977.
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In recent years, under the background of stable economic operation, people's research on economic systems has become increasingly popular. The dynamic input-output model reflects the change and development process of the input-output relationship of the economic system over a period of time. The main purpose of tracking control is to design a suitable controller so that the output of the control system can track the output of the reference system as much as possible. In the economic system, data is an important factor. Based on this, this paper mainly studies the tracking control of dynamic input-output economic system based on data fusion. This research takes the data fusion of the dynamic input-output economic system as the starting point and takes the optimal control and tracking of the economic system as the research object of this research. Based on data fusion technology, a new dynamic input-output economic system tracking and control is proposed. This paper studies the finite-time optimal tracking control of linear systems. Through numerical examples, comparing the finite-time and infinite-time optimal control simulation results, it is proved that the algorithm can achieve good tracking control. Experimental data shows that the optimal and suboptimal performance indicators for a limited time are 0.7729412 and 1.5687310, respectively. Therefore, compared with the infinite-time optimal control, the performance loss and the final tracking error of the suboptimal control proposed in this study are reduced.
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Bhatt, Maharshi K., Bhavin S. Sedani, and Komal Borisagar. "Performance analysis of massive multiple input multiple output for high speed railway." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 5180. http://dx.doi.org/10.11591/ijece.v11i6.pp5180-5188.
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This paper analytically reviews the performance of massive multiple input multiple output (MIMO) system for communication in highly mobility scenarios like high speed Railways. As popularity of high speed train increasing day by day, high data rate wireless communication system for high speed train is extremely required. 5G wireless communication systems must be designed to meet the requirement of high speed broadband services at speed of around 500 km/h, which is the expected speed achievable by HSR systems, at a data rate of 180 Mbps or higher. Significant challenges of high mobility communications are fast time-varying fading, channel estimation errors, doppler diversity, carrier frequency offset, inter carrier interference, high penetration loss and fast and frequent handovers. Therefore, crucial requirement to design high mobility communication channel models or systems prevails. Recently, massive MIMO techniques have been proposed to significantly improve the performance of wireless networks for upcoming 5G technology. Massive MIMO provide high throughput and high energy efficiency in wireless communication channel. In this paper, key findings, challenges and requirements to provide high speed wireless communication onboard the high speed train is pointed out after thorough literature review. In last, future research scope to bridge the research gap by designing efficient channel model by using massive MIMO and other optimization method is mentioned.
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Gao, Zhiqiang, Bruce Tabachnik, and Razvan V. Savescu. "Transfer function matrix identification from input—output frequency response data." Journal of the Franklin Institute 331, no. 4 (July 1994): 435–48. http://dx.doi.org/10.1016/0016-0032(94)90007-8.
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Wang, Zhaocheng, and Jiaxuan Chen. "Networked multiple-input-multiple-output for optical wireless communication systems." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2169 (March 2, 2020): 20190189. http://dx.doi.org/10.1098/rsta.2019.0189.
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With the escalation of heterogeneous data traffic, the research on optical wireless communication (OWC) has attracted much attention, owing to its advantages such as wide spectrum, low power consumption and high security. Ubiquitous optical devices, e.g. light-emitting diodes (LEDs) and cameras, are employed to support optical wireless links. Since the distribution of these optical devices is usually dense, multiple-input-multiple-output (MIMO) can be naturally adopted to attain spatial diversity gain or spatial multiplexing gain. As the scale of OWC networks enlarges, optical MIMO can also collaborate with network-level operations, like user/AP grouping, to enhance the network throughput. Since OWC is preferred for short-range communications and is sensitive to the directions/rotations of transceivers, optical MIMO links vary frequently and sharply in outdoor scenarios when considering the mobility of optical devices, raising new challenges to network design. In this work, we present an overview of optical MIMO techniques, as well as the cooperation of MIMO and user/AP grouping in OWC networks. In consideration of the challenges for outdoor OWC, key technologies are then proposed to facilitate the adoption of optical MIMO in outdoor scenarios, especially in vehicular ad hoc networks. Lastly, future applications of MIMO in OWC networks are discussed. This article is part of the theme issue ‘Optical wireless communication’.
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Zeng, Lubin, Dominic O'Brien, Hoa Minh, Grahame Faulkner, Kyungwoo Lee, Daekwang Jung, Yunje Oh, and Eun Won. "High data rate multiple input multiple output (MIMO) optical wireless communications using white led lighting." IEEE Journal on Selected Areas in Communications 27, no. 9 (December 2009): 1654–62. http://dx.doi.org/10.1109/jsac.2009.091215.
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Xu, Yahong, Geng Yang, and Shuangjie Bai. "Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis." Security and Communication Networks 2019 (November 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/9169802.
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With the widespread application of big data, privacy-preserving data analysis has become a topic of increasing significance. The current research studies mainly focus on privacy-preserving classification and regression. However, principal component analysis (PCA) is also an effective data analysis method which can be used to reduce the data dimensionality, commonly used in data processing, machine learning, and data mining. In order to implement approximate PCA while preserving data privacy, we apply the Laplace mechanism to propose two differential privacy principal component analysis algorithms: Laplace input perturbation (LIP) and Laplace output perturbation (LOP). We evaluate the performance of LIP and LOP in terms of noise magnitude and approximation error theoretically and experimentally. In addition, we explore the variation of performance of the two algorithms with different parameters such as number of samples, target dimension, and privacy parameter. Theoretical and experimental results show that algorithm LIP adds less noise and has lower approximation error than LOP. To verify the effectiveness of algorithm LIP, we compare our LIP with other algorithms. The experimental results show that algorithm LIP can provide strong privacy guarantee and good data utility.
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Najadat, Hassan, Ahmad Alaiad, Sanaa Abu Alasal, Ghadeer Anwar Mrayyan, and Izzat Alsmadi. "Integration of Data Envelopment Analysis and Clustering Methods." Journal of Information & Knowledge Management 19, no. 01 (March 2020): 2040006. http://dx.doi.org/10.1142/s0219649220400067.
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Data Envelopment Analysis (DEA) has been applied creatively in various study domains to compare and evaluate different Decision Making Units (DMUs) based on multiple input–output attributes. In this paper, the performance of Jordanian public hospitals is assessed via a methodology combining DEA with data mining methods, specifically, clustering. Initially, inputs of inefficient hospitals were altered to check for waste in the allocated resources. Then, the number of inputs–outputs was manipulated to test if the number is strongly influencing the productivity of the DMUs. The number of DMUs used was 27 public hospitals and the applicable efficiency measurements used were constant return to scale (CRS) and variable return to scale (VRS) through the DEAP software. Experiments showed that the efficiency of a hospital might be more meaningfully assessed if it is compared with a group of hospitals that are similar in some factors. More specifically, results of applying the CRS model proved that 77% of the hospitals were efficient. Additionally, we found that the inefficiencies of some hospitals are linked to weak resource utilization. It is concluded that number of inputs–outputs inserted in the efficiency evaluation process impacts the resulted values.
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Subramani, Prabu, Ganesh Babu Rajendran, Jewel Sengupta, Rocío Pérez de Prado, and Parameshachari Bidare Divakarachari. "A Block Bi-Diagonalization-Based Pre-Coding for Indoor Multiple-Input-Multiple-Output-Visible Light Communication System." Energies 13, no. 13 (July 4, 2020): 3466. http://dx.doi.org/10.3390/en13133466.
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Visible Light Communication (VLC) is a promising field in optical wireless communications, which uses the illumination infrastructure for data transmission. The important features of VLC are electromagnetic interference-free, license-free, etc. Additionally, Multiple-Input-Multiple-Output (MIMO) techniques are enabled in the VLC for enhancing the limited modulation bandwidth by its spectral efficiency. The data transmission through the MIMO-VLC system is corrupted by different interferences, namely thermal noise, shot noise and phase noise, which are caused by the traditional fluorescent light. In this paper, an effective precoding technique, namely Block Bi-Diagonalization (BBD), is enabled to mitigate the interference occurring in the indoor MIMO-VLC communications. Besides, a Quadrature Amplitude Modulation (QAM) is used to modulate the signal before transmission. Here, the indoor MIMO-VLC system is developed to analyze the communication performance under noise constraints. The performance of the proposed system is analyzed in terms of Bit Error Rate (BER) and throughput. Furthermore, the performances are compared with three different existing methods such as OAP, FBM and NRZ-OOK-LOS. The BER value of the proposed system of scenario 1 is 0.0501 at 10 dB, which is less than that of the FBM technique.
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Sadeghi, Maryam, and Majid Gholami. "Developing Adaptive Neuro-Fuzzy Inference System for Controlling the Intelligent Universal Transformers in ADA." Advanced Materials Research 433-440 (January 2012): 3969–73. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3969.
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This approach is carry out for developing the Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling the forthcoming Intelligent Universal Transformer (IUT) in regard of voltages and current control in both input and output stages which is optimized by particle swarm optimization. Current or voltages errors and their time derivative have been considered as the inputs of Nero Fuzzy controller for elaborating the firing angles of converters in IUT basic construction. ANFIS constructed from a fuzzy inference system (FIS) in which the membership function parameters are tuned according to the back propagation algorithm or in conjunction to the least squares method. A neural network maps inputs through input membership functions and associated parameters, and output membership functions and associated parameters to outputs which interprets the input-output map. The associated parameters of membership functions change through the learning algorithm by a gradient vector modeling the input output data in case of given parameters. Optimization method will be investigated to adjust the parameters according to error reduction computed by sum of the squared variation from actual outputs to the desired ones. Advanced Distribution Automation (ADA) is the state of art introducing for tomorrows distribution automation with the new invention in management and control. ADA is equipping by the Intelligent Equipment Devices (IED) in which IUT is a key point introducing as an intelligent transformer subjecting for tomorrows distribution automation in the near future. The proposed ANFIS is a control scheme develop for controlling the IUT by bringing the major advantages like harmonic Filtering, voltage regulation, automatic sag correction, energy storage, 48V DC option, three phase outputs in term of one phase input, reliable divers power as 240V 400HZ for communication utilization and two other 240V 60 HZ outputs, dynamic system monitoring and robustness in major disturbances occurred in terms of input and load variation.
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Song, Ruizhuo, Frank Lewis, Qinglai Wei, Hua-Guang Zhang, Zhong-Ping Jiang, and Dan Levine. "Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data." IEEE Transactions on Neural Networks and Learning Systems 26, no. 4 (April 2015): 851–65. http://dx.doi.org/10.1109/tnnls.2015.2399020.
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Romano, Gianmarco. "Editorial: Special Issue “Massive MIMO and mm-Wave Communications”." Sensors 22, no. 2 (January 11, 2022): 519. http://dx.doi.org/10.3390/s22020519.
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Massive multiple-input multiple-output (mMIMO) communication systems and the use of millimeter-wave (mm-Wave) bands represent key technologies that are expected to meet the growing demand of data traffic and the explosion of the number of devices that need to communicate over 5G/6G wireless networks [...]
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Yang, Chao, Feng He, and Chang Ren. "Environmental Efficiency Evaluation Method Based on Data Envelopment Analysis and Improved Neural Network." Mobile Information Systems 2021 (November 15, 2021): 1–7. http://dx.doi.org/10.1155/2021/3766980.
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Economic development in China requires lots of energy to support it, but how to acquire an adequate energy supply is a difficult problem. Meantime, environmental pollution caused by energy consumption is a problem that immediately needs to be solved. To adapt to China’s rapidly emerging economy, and based on existing policies, giving more consideration to energy saving and environmental safety is more important. Therefore, to investigate China’s regional environmental efficiency and its factors has key importance. In order to evaluate the environmental efficiency input in China, this study first selects some indexes of environmental efficiency and applies the Data Envelopment Analysis (DAE) method to measure the efficiency of input and output. Then, the relative index of environmental efficiency input is selected as the input variable and the efficiency value as the output variable. The Backpropagation neural network is employed to learn and establish the prediction model and achieve high prediction accuracy. The performance of the model is improved by optimizing the index of environmental efficiency investment, adopting the latest data, and increasing the learning samples. This method is not only suitable for the evaluation of macro-environmental efficiency investment, but also suitable for enterprises in specific industries.
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Shakir, Farouk, Mazin Ali A. Ali, and Firas Ameer. "Utilization of MIMO Concept for Optical Communication System under Fog Condition." ECTI Transactions on Electrical Engineering, Electronics, and Communications 17, no. 2 (August 31, 2019): 130–35. http://dx.doi.org/10.37936/ecti-eec.2019172.219183.
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Free-space optical (FSO) communication consider license free, high data rate, wide bandwidth and cost-effective. Multi-input Multi-output (MIMO) systems can be employed to reduce the attenuation by heavy fog and improve FSO channel capacity. In this paper a single-input single-output and multi–input multi-output examined to investigate the performance of these systems under heavy fog. A comparison is made in terms of received optical power, signal to noise ratio, and bit error rate (BER) using OptiSystem version 7.0. The signal reaches to link up to 1.7km, 1.55km, 1.5km, and 1.4km for 4Tx/4Rx, 3Tx/3Rx, 2Tx/2Rx, 1Tx/1Rxrespectively. The results showed that the quality of received power is enhancement by using up to four beams.
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Takagi, Taro, and Ikuro Mizumoto. "PFC design method with input/output data of controlled system via DE." Electronics and Communications in Japan 101, no. 9 (July 20, 2018): 48–54. http://dx.doi.org/10.1002/ecj.12109.
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Song, Aijun, Mohsen Badiey, Vincent Keyko McDonald, and T. C. Yang. "Time Reversal Receivers for High Data Rate Acoustic Multiple-Input–Multiple-Output Communication." IEEE Journal of Oceanic Engineering 36, no. 4 (October 2011): 525–38. http://dx.doi.org/10.1109/joe.2011.2166660.
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Sait, Sadiq M., and Ghalib A. Al-Hashim. "Novel Design of Heterogeneous Automation Controller Based on Real-Time Data Distribution Service Middleware to Avoid Obsolescence Challenges." Journal of Circuits, Systems and Computers 25, no. 09 (June 21, 2016): 1650111. http://dx.doi.org/10.1142/s0218126616501115.
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Oil and gas processing facilities utilize various process automation systems with proprietary controllers. As the systems age; older technologies become obsolete resulting in frequent premature capital investments to sustain their operation. This paper presents a new design of automation controller to provide inherent mechanisms for upgrades and/or partial replacement of any obsolete components without obligation for a complete system replacement throughout the expected life cycle of the processing facilities. The input/output racks are physically and logically decoupled from the controller by converting them into distributed autonomous process interface systems. The proprietary input/output communication between the conventional controller CPU and the associated input/output racks is replaced with standard real-time data distribution service middleware for providing seamless cross-vendor interoperable communication between the controller and the distributed autonomous process interface systems. The objective of this change is to allow flexibility of supply for all controller’s subcomponents from multiple vendors to safeguard against premature automation obsolescence challenges. Detailed performance analysis was conducted to evaluate the viability of using the standard real-time data distribution service middleware technology in the design of automation controller to replace the proprietary input/output communication. The key simulation measurements to demonstrate its performance sustainability while growing in controller’s size based on the number of input/output signals are communication latency, variation in packets delays, and communication throughput. The overall performance results confirm the viability of the new proposal as the basis for designing cost effective evergreen process automation solutions that would result in optimum total cost of ownership capital investment throughout the systems’ life span. The only limiting factor is the selected network infrastructure.
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Junior Momo Ziazet, Charles Boudreau, Brigitte Jaumard, and Huy Duong. "Addressing RouteNet scalability through input and output design." ITU Journal on Future and Evolving Technologies 3, no. 2 (September 22, 2022): 224–34. http://dx.doi.org/10.52953/giod4389.
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With recent advances in the field of Machine Learning (ML), a multitude of problems related to communication systems and networks can be solved with data-driven solutions. Since data in these systems is mostly represented as graphs, Graph-based Neural Networks (GNNs) are a good candidate for solving such problems. These GNNs can be used as a computer network modeling technique to build models that accurately estimate the Key Performance Indicators (KPI) such as delay or jitter in real network scenarios in order to ensure their requirements in terms of service assurance. To build GNN solutions with higher accuracy, low computational resource requirements, and easy deployment of synthetic network training results into real-world networks, it is more than necessary to develop efficient and effective GNN models. This paper presents a GNN model capable of accurately estimating the average delay per flow in networks. By designing scale-independent features and using notions from queuing theory, the proposed model successfully generalizes to large size topologies, routing configurations, and traffic matrices not seen during the training phase.
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Nakatani, Yuki, Takuya Kinoshita, and Toru Yamamoto. "Design of a Data-Driven Control System based on Reference Model using Predicted Input/Output Responses." Journal of Robotics, Networking and Artificial Life 8, no. 3 (2021): 170. http://dx.doi.org/10.2991/jrnal.k.210922.004.
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Srinivasan, Santhoshkumar, and Dhinesh Babu L.D. "A Neuro-Fuzzy Approach to Detect Rumors in Online Social Networks." International Journal of Web Services Research 17, no. 1 (January 2020): 64–82. http://dx.doi.org/10.4018/ijwsr.2020010104.
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Along with true information, rumors spread in online social networks (OSN) on an unprecedented scale. In recent days, rumor identification gains more interest among the researchers. Finding rumors also poses other critical challenges like noisy and imprecise input data, data sparsity, and unclear interpretations of the output. To address these issues, we propose a neuro-fuzzy classification approach called the neuro-fuzzy rumor detector (NFRD) to automatically identify the rumors in OSNs. NFRD quickly transforms the input to fuzzy rules which classify the rumor. Neural networks handle larger input data. Fuzzy systems are better in handling uncertainty and imprecision in input data by producing fuzzy rules that effectively eliminate the unclear inputs. NFRD also considers the semantic aspects of information to ensure better classification. The neuro-fuzzy approach addresses the most common problems such as uncertainty elimination, noise reduction, and quicker generalization. Experimental results show the proposed approach performs well against state-of-the-art rumor detecting techniques.
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Djennoune, Said, Maamar Bettayeb, and Ubaid Muhsen Al-Saggaf. "Synchronization of fractional–order discrete–time chaotic systems by an exact delayed state reconstructor: Application to secure communication." International Journal of Applied Mathematics and Computer Science 29, no. 1 (March 1, 2019): 179–94. http://dx.doi.org/10.2478/amcs-2019-0014.
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Abstract This paper deals with the synchronization of fractional-order chaotic discrete-time systems. First, some new concepts regarding the output-memory observability of non-linear fractional-order discrete-time systems are developed. A rank criterion for output-memory observability is derived. Second, a dead-beat observer which recovers exactly the true state system from the knowledge of a finite number of delayed inputs and delayed outputs is proposed. The case of the presence of an unknown input is also studied. Third, secure data communication based on a generalized fractional-order Hénon map is proposed. Numerical simulations and application to secure speech communication are presented to show the efficiency of the proposed approach.
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Ibrahim, Sarmad Khaleel, and Saif A. Abdulhussien. "Performance evaluation of precoding system for massive multiple-input multiple-output." Bulletin of Electrical Engineering and Informatics 11, no. 4 (August 1, 2022): 2054–61. http://dx.doi.org/10.11591/eei.v11i4.3877.
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Low latency, high data speeds, and a higher degree of perceived service quality for consumers and base station capacity are only some of the advantages of fifth generation (5G) mobile communications. This paper focuses on the design of a precoding system for downlink transmission of multi-user multiple-input multiple-output (MU-MIMO). For MU-MIMO systems, the traditional precoding techniques investigated are difficult since the transmitter precoding matrices created by singular value decomposition (SVD) are calculated twice. This paper implements different techniques of precoding with channel coding. Two advanced precoding, zero forcing (ZF) and maximum ratio transmitter (MRT) systems will be evaluated to find the best between them. Three different coding channels (turbo, low-density parity-check (LDPC), and polar) are used in this paper. The results indicate that the ZF-MU-MIMO with turbo coding outperforms MRT precoding, and more spatial diversity gain may be gained, in terms of throughput, number of users supported, and lower error rate in downlink and uplink massive MIMO.
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Jatmika, Ovan Bagus. "Faktor Penunjang Pertunjukan Musik: Input, Proses, dan Output." Journal of Music Science, Technology, and Industry 3, no. 1 (January 31, 2020): 79–90. http://dx.doi.org/10.31091/jomsti.v3i1.966.
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This research examines the supporting factors in improving the quality of musical performances. The method used is qualitative and the research data are found through library research. The author concludes that a performance is considered to have ideal conditions if it involves three elements that support each other in the process towards the end of the musical execution on the stage, namely input, process, and output. These three elements (input, process, and output) are expected to improve the quality of the performance. The intended form of quality improvement is the formation of two-way communication or dialogue between the player and the audience. The player conveys the musical content of their interpretation of the work being played, while the audience responds to the standard measure of musical, visual and gestural norms that they have / believe in.
Keywords: musical stimuli, musical emotions, learning motivation, exercise effectiveness, musical memory.
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GARCÍA-CUESTA, ESTEBAN, INÉS M. GALVÁN, and ANTONIO J. DE CASTRO. "RECURSIVE DISCRIMINANT REGRESSION ANALYSIS TO FIND HOMOGENEOUS GROUPS." International Journal of Neural Systems 21, no. 01 (February 2011): 95–101. http://dx.doi.org/10.1142/s0129065711002663.
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The main motivation of this paper is to propose a method to extract the output structure and find the input data manifold that best represents that output structure in a multivariate regression problem. A graph similarity viewpoint is used to develop an algorithm based on LDA, and to find out different output models which are learned as an input subspace. The main novelty of the algorithm is related with finding different structured groups and apply different models to fit better those structures. Finally, the proposed method is applied to a real remote sensing retrieval problem where we want to recover the physical parameters from a spectrum of energy.
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Chang, Shu-Hao. "Patent Technology Network Analysis of Machine-Learning Technologies and Applications in Optical Communications." Photonics 7, no. 4 (December 15, 2020): 131. http://dx.doi.org/10.3390/photonics7040131.
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As the Internet of Things (IoT) develops, applying machine learning on optical communications has become a prospective field of research. Scholars have mostly concentrated on algorithmic techniques or specific applications but have been unable to address the distribution of machine-learning technologies and the development of its applications in optical communications from a macro perspective. Therefore, in this paper, machine-learning patents in optical communications are taken as the analytical basis for constructing a patent technology network. The study results revealed that key technologies were primarily in data input and output devices, data-processing methods, wireless communication networks, and the transmission of digital information in optical communications. Such technologies were also applied to perform measurement for diagnostic purposes and medical diagnoses. The technology network model proposed in this paper explores the technological development trends of machine learning in optical communications and serves as a reference for allocating research and development resources.
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Junior, Milembolo Miantezila, Bin Guo, Chenjie Zhang, and Xuemei Bai. "Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems." Sensors 21, no. 11 (May 21, 2021): 3584. http://dx.doi.org/10.3390/s21113584.
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Cellular network operators are predicting an increase in space of more than 200 percent to carry the move and tremendous increase of total users in data traffic. The growing of investments in infrastructure such as a large number of small cells, particularly the technologies such as LTE-Advanced and 6G Technology, can assist in mitigating this challenge moderately. In this paper, we suggest a projection study in spectrum sharing of radar multi-input and multi-output, and mobile LTE multi-input multi-output communication systems near m base stations (BS). The radar multi-input multi-output and mobile LTE communication systems split different interference channels. The new approach based on radar projection signal detection has been proposed for free interference disturbance channel with radar multi-input multi-output and mobile LTE multi-input multi-output by using a new proposed interference cancellation algorithm. We chose the channel of interference with the best free channel, and the detected signal of radar was projected to null space. The goal is to remove all interferences from the radar multi-input multi-output and to cancel any disturbance sources from a chosen mobile Communication Base Station. The experimental results showed that the new approach performs very well and can optimize Spectrum Access.
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Sun, Lin, Ming Yan, Haisen Li, and Yanjie Xu. "Joint Time-Reversal Space-Time Block Coding and Adaptive Equalization for Filtered Multitone Underwater Acoustic Communications." Sensors 20, no. 2 (January 9, 2020): 379. http://dx.doi.org/10.3390/s20020379.
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Underwater acoustic (UWA) sensor networks demand high-rate communications with high reliability between sensor nodes for massive data transmission. Filtered multitone (FMT) is an attractive multicarrier technique used in high-rate UWA communications, and can obviously shorten the span of intersymbol interference (ISI) with high spectral efficiency and low frequency offset sensitivity by dividing the communication band into several separated wide sub-bands without guard bands. The joint receive diversity and adaptive equalization scheme is often used as a general ISI suppression technique in FMT-UWA communications, but large receive array for high diversity gain has an adverse effect on the miniaturization of UWA sensor nodes. A time-reversal space-time block coding (TR-STBC) technique specially designed for frequency-selective fading channels can replace receive diversity with transmit diversity for high diversity gain, and therefore is helpful for ISI suppression with simple receive configuration. Moreover, the spatio-temporal matched filtering (MF) in TR-STBC decoding can mitigate ISI obviously, and therefore is of benefit to lessen the complexion of adaptive equalization for post-processing. In this paper, joint TR-STBC and adaptive equalization FMT-UWA communication method is proposed based on the merit of TR-STBC. The proposed method is analyzed in theory, and its performance is assessed using simulation analysis and real experimental data collected from an indoor pool communication trial. The validity of the proposed method is proved through comparing the proposed method with the joint single-input–single-output (SISO) and adaptive equalization method and the joint single-input–multiple-output (SIMO) and adaptive equalization method. The results show that the proposed method can achieve better communication performance than the joint SISO and adaptive equalization method, and can achieve similar performance with more simpler receive configuration as the joint SIMO and adaptive equalization method.
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Hafizh, Muhammad Nur, and M. Handry Imansyah. "Identifikasi Sektor Potensial pada Perekonomian Kabupaten Banjar: Analisis Input-Output." JIEP: Jurnal Ilmu Ekonomi dan Pembangunan 4, no. 2 (November 26, 2021): 386. http://dx.doi.org/10.20527/jiep.v4i2.4395.
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This study aims to determine the potential sectors in improving the economy of Banjar Regency. This study uses input-output analysis to identify potential sectors through the calculation of Forward & Backward Linkage, and Multiplier effects. Input-output table was constructed by deriving Kalimantan Selatan Input-Output Table with an adjustment using Quotient Location. An adjustment using secondary data such as Gross Regional Domestic Product by sectors and by Expenditure, including employment data by sectors in Kabupaten BanjarThe results showed that (1) the processing industry; (2) Transportation, Warehousing and Communication; (3) Finance, Real Estate and Company Services; (4) Electricity, Gas and Water, (5) Construction is a potential sector in Banjar Regency. This sector needs to be developed because expanding this sector is expected to encourage other sectors.The limitations of this research are the limited data obtained and the data used are only the results of documentation by the related agencies.
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JAHROMI, HAMED DEHDASHTI, ALI BINAIE, ABBAS ZARIFKAR, and MOHAMMAD HOSSEIN SHEIKHI. "A NEW STRUCTURE FOR ALL-OPTICAL THREE-INPUT XOR LOGIC GATE BASED ON SEMICONDUCTOR OPTICAL AMPLIFIER MACH–ZEHNDER INTERFEROMETER." International Journal of Modern Physics B 28, no. 07 (February 20, 2014): 1450052. http://dx.doi.org/10.1142/s0217979214500520.
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In this paper the design and simulation of an all-optical three-input exclusive-OR (XOR) gate based on semiconductor optical amplifier-Mach–Zehnder interferometer (SOA-MZI) configuration is presented. This gate is constructed of three semiconductor optical amplifiers placed at the arms of the two coupled Mach–Zehnder interferometers. The outputs of these two MZIs are combined at the output port to form the final signal. The proposed scheme does not need a continuous wave (CW) laser for its operation and is designed so that the minimum number SOAs are employed. Theoretical analyses and simulations show that this structure acts as an all-optical three-input XOR logic gate which has many applications in optical data communications.
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Lawrence, Nicholas P., Brian W. H. Ng, Hedley J. Hansen, and Derek Abbott. "Analysis of millimetre-wave polarization diverse multiple-input multiple-output capacity." Royal Society Open Science 2, no. 12 (December 2015): 150322. http://dx.doi.org/10.1098/rsos.150322.
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Millimetre-waves offer the possibility of wide bandwidth and consequently high data rate for wireless communications. For both uni- and dual-polarized systems, signals sent over a link may suffer severe degradation due to antenna misalignment. Orientation robustness may be enhanced by the use of mutual orthogonality in three dimensions. Multiple-input multiple-output polarization diversity offers a way of improving signal reception without the limitations associated with spatial diversity. Scattering effects often assist propagation through multipath. However, high path loss at millimetre-wave frequencies may limit any reception enhancement through scattering. We show that the inclusion of a third orthogonal dipole provides orientation robustness in this setting, as well as in a rich scattering environment, by means of a Rician fading channel model covering all orientations for a millimetre-wave, tri-orthogonal, half-wave dipole transmitter and receiver employing polarization diversity. Our simulation extends the analysis into three dimensions, fully exploiting individual sub-channel paths. In both the presence and absence of multipath effects, capacity is observed to be higher than that of a dual-polarized system over the majority of a field of view.
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Jehan, Shahzadah Nayyar, and Mudalige Uthpala Indeelinie Alahakoon. "Digitalization of Public Services—An Input Output Logit Analysis." Applied System Innovation 3, no. 4 (December 3, 2020): 56. http://dx.doi.org/10.3390/asi3040056.
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In 2000, Sri Lanka designed an ambitious plan for the introduction of information and communication technology (ICT) in most government functions and in the public service delivery (PSD) system in the country. This process started in the early 2000s and gained momentum with several local and internationally funded initiatives. A systematic innovation concept was incorporated within the ICT regime, which ensured bottom-up learning for a smooth transformation from paper to digitized PSD systems. Towards this end, the Information Communication Technology Agency (ICTA) and Lanka Government Network (LGN) were established. ICT incorporation covered the operations of most government agencies and departments to improve governance and PSD. We analyzed the efficiency of the ICT regime to understand its impact on public service employee output as well as on services to the public. We collected service delivery data from both the employees and their clients using a Likert-scale questionnaire. The questionnaire enquired about the utility of the ICT regime introduced in various departments and ministries (DMs) of the Sri Lankan government. This paper analyzes the overall and relative effectiveness of the ICT regime in terms of the inputs incurred and the outcomes realized. First, we calculated the Cronbach’s alpha to test the robustness of the data. Second, we applied ordinal logistics analysis to understand the interrelations among various measures (inputs) and their impacts (outcomes). Finally, we conducted specificity, sensitivity, and predictive value analysis to assess the accuracy of the investigative model. Our findings suggest a positive correlation between the inputs and the outcomes of the ICT regime introduced to digitalize PSD. Our results further indicate that although the inputs and the outcomes are positively corelated, this correlation is not sufficiently strong, and the ICT implementation measures need further emphasis to demonstrate any significant impact on user confidence in this regime.
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Gedeon, Tamás D. "Data Mining of Inputs: Analysing Magnitude and Functional Measures." International Journal of Neural Systems 08, no. 02 (April 1997): 209–18. http://dx.doi.org/10.1142/s0129065797000227.
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The problem of data encoding and feature selection for training back-propagation neural networks is well known. The basic principles are to avoid encrypting the underlying structure of the data, and to avoid using irrelevant inputs. This is not easy in the real world, where we often receive data which has been processed by at least one previous user. The data may contain too many instances of some class, and too few instances of other classes. Real data sets often include many irrelevant or redundant input fields. This paper examines the use of weight matrix analysis techniques and functional measures using two real (and hence noisy) data sets. The first part of this paper examines the use of the weight matrix of the trained neural network itself to determine which inputs are significant. A new technique is introduced and compared with two other techniques from the literature. We present our experience and results on some satellite data augmented by a terrain model. The task was to predict the forest supra-type based on the available information. A brute force technique eliminating randomly selected inputs was used to validate our approach. The second part of this paper examines the use of measures to determine the functional contribution of inputs to outputs. Inputs which include minor but unique information to the network are more significant than inputs with higher magnitude contribution but providing redundant information, which is also provided by another input. A comparison is made to sensitivity analysis, where the sensitivity of outputs to input perturbation is used as a measure of the significance of inputs. This paper presents a novel functional analysis of the weight matrix based on a technique developed for determining the behavioral significance of hidden neurons. This is compared with the application of the same technique to the training and test data. Finally, a novel aggregation technique is introduced.
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Abouda, A. A., and N. G. Tarhuni. "MIMO Channel Gain Mechanisms Relative to SISO Channel." Journal of Engineering Research [TJER] 7, no. 2 (December 1, 2010): 40. http://dx.doi.org/10.24200/tjer.vol7iss2pp40-47.
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In contrast to the rest of wireless communication technologies, multiple-input multiple-output (MIMO) technology enjoys different gain mechanisms that make it very attractive for reliable high data rate wireless communications. This paper presents a study on these gain mechanisms with particular emphasis on the case of high average received signal to noise ratio (SNR) where the MIMO system deployment is most promising. We write the MIMO channel capacity in terms of gains relative to a single- input single-output (SISO) wireless channel. Doing so, spatial multiplexing gain and power gain of MIMO wireless channels become more insightful. Based on this analysis a switching scheme between spatial multiplexing and transmit diversity is proposed. We support our discussion with numerical results which show that under a high data rate spatial multiplexing scheme the contribution of each gain mechanism to the total channel capacity depends on the channel Ricean factor, the average received SNR, and the MIMO system size. The proposed switching scheme gives about 2 dB gain in bit error rate performance relative to the spatial multiplexing mode.
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Nijman, Marcel J., and Hilbert J. Kappen. "Symmetry Breaking and Training from Incomplete Data with Radial Basis Boltzmann Machines." International Journal of Neural Systems 08, no. 03 (June 1997): 301–15. http://dx.doi.org/10.1142/s0129065797000318.
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A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.
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Zhang, Xianyu, Daoxing Guo, Kongzhe Yang, and Silin Xie. "Secure downlink transmission with finite resolution analog beamforming in massive multiple-input multiple-output system." International Journal of Distributed Sensor Networks 14, no. 9 (September 2018): 155014771880225. http://dx.doi.org/10.1177/1550147718802254.
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To reduce the number of radio-frequency chains of base station, the use of finite resolution analog beamforming is desirbale in massive multiple-input multiple-output system. This article investigates the secure downlink massive multiple-input multiple-output data transmission with artificial noise at base station in the presence of a multi-antenna passive eavesdropper. The achievable user’s ergodic information rate and ergodic capacity of the eavesdropper are analyzed in detail, respectively. With maximum ratio transmission or maximum ratio combining, we derive closed-form expressions for a tight lower bound on ergodic secrecy rate and tight upper bound for secrecy outage probability. Based on these analytical expressions, the effects of various system parameters on secrecy performance, such as power allocation factor, number of eavesdropper’s antennas, number of the user terminals, total transmission power, and finite resolution analog beamforming parameters, are investigated in detail. Also, the optimal power allocation scheme between data and artificial noise signals is achieved in closed form to maximize the ergodic secrecy rate. In addition, we derive the conditions that the secure massive multiple-input multiple-output system need to meet to obtain a positive secrecy rate. Finally, numerical simulation results validate the system’s secrecy performance and verify all the theoretical analytical results.
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Yu, Hai Yan, Hai Bo Meng, Qi Song, and Tian Yong Deng. "Research on Data Acquisition System of Ship Measurement and Control Device Check Equipment." Advanced Materials Research 591-593 (November 2012): 1242–46. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1242.
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Ship measurement and control device check equipment has realized the intelligent check of ship measurement and control device’s functional modules which have the function of parameter measuring, data acquisition, calculation, communication and power supply.It need to complete multiple parameters’data acquisition and operation above all, this paper introduces the process of data acquisition and operation of industrial control computer’s PCI cards by using VC program. The PCI cards include PCI-1713,PCI-1724,PCI-1758UDI,PCI-1758UDO,PCI-1612CU.It realizes the data acquisition and operation of industrial control computer’s analog input\output PCI cards,digital input\output PCI cards and communication PCI card. It completes the data acquisition function of check equipment. it satisfys the need of realtime and high accuracy of check equipment,it can used to other data acquisition systems.
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Nawaz Jadoon, Rab, Mohsin Fayyaz, WuYang Zhou, Muhammad Amir Khan, and Ghulam Mujtaba. "PCOI: Packet Classification‐Based Optical Interconnect for Data Centre Networks." Mathematical Problems in Engineering 2020 (July 17, 2020): 1–11. http://dx.doi.org/10.1155/2020/2903157.
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To support cloud services, Data Centre Networks (DCNs) are constructed to have many servers and network devices, thus increasing the routing complexity and energy consumption of the DCN. The introduction of optical technology in DCNs gives several benefits related to routing control and energy efficiency. This paper presents a novel Packet Classification based Optical interconnect (PCOI) architecture for DCN which simplifies the routing process by classifying the packet at the sender rack and reduces energy consumption by utilizing the passive optical components. This architecture brings some key benefits to optical interconnects in DCNs which include (i) routing simplicity, (ii) reduced energy consumption, (iii) scalability to large port count, (iv) packet loss avoidance, and (v) all-to-one communication support. The packets are classified based on destination rack and are arranged in the input queues. This paper presents the input and output queuing analysis of the PCOI architecture in terms of mathematical analysis, the TCP simulation in NS2, and the physical layer analysis by conducting simulation in OptiSystem. The packet loss in the PCOI has been avoided by adopting the input and output queuing model. The output queue of PCOI architecture represents an M/D/32 queue. The simulation results show that PCOI achieved a significant improvement in terms of throughput and low end-to-end delay. The eye-diagram results show that a good quality optical signal is received at the output, showing a very low Bit Error Rate (BER).
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Odeyemi, Kehinde, and Erastus Ogunti. "Performance Comparison of Multiple Input Multiple Output Techniques for High Data Rate Wireless Communication System." Journal of Computational Intelligence and Electronic Systems 3, no. 1 (March 1, 2014): 36–43. http://dx.doi.org/10.1166/jcies.2014.1061.
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Almradi, Ahmed M., and Sohail A. Dianat. "NDA SNR and CRLB Estimation Over MISO with STBC Channels." International Journal of Business Data Communications and Networking 8, no. 4 (October 2012): 1–16. http://dx.doi.org/10.4018/jbdcn.2012100101.
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This paper discusses the problem of Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation of Binary Phase Shift keying (BPSK) modulated signals using the Expectation Maximization (EM) Algorithm. In addition, the Cramer-Rao Lower Bounds (CRLB) for the estimation of Data Aided (DA) and Non Data Aided (NDA) Signal to Noise Ratio (SNR) estimation is derived. Multiple Input Single Output (MISO) channels with Space Time Block Codes (STBC) is used. The EM algorithm is a method that finds the Maximum Likelihood (ML) solution iteratively when there are unobserved (hidden or missing) data. Extension of the proposed approach to other types of linearly modulated signals in estimating SNR is straight forward. The performance of the estimator is assessed using the NDA CRLBs. Alamouti coding technique is used in this paper with two transmit antennas and one receive antenna. The authors’ assumption is that the received signal is corrupted by additive white Gaussian noise (AWGN) with unknown variance, and scaled by fixed unknown complex channel gain. Monte Carlo simulations are used to show that the proposed estimator offers a substantial improvement over the conventional Single Input Single Output (SISO) NDA SNR estimator due to the use of the statistical dependences in space and time. Moreover, the proposed NDA SNR estimator works close to the NDA SNR estimator over Single Input Multiple Output (SIMO) channels.
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Tayyaba, Shahzadi, Muhammad Waseem Ashraf, Thamer Alquthami, Zubair Ahmad, and Saher Manzoor. "Fuzzy-Based Approach Using IoT Devices for Smart Home to Assist Blind People for Navigation." Sensors 20, no. 13 (June 30, 2020): 3674. http://dx.doi.org/10.3390/s20133674.
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The demand of devices for safe mobility of blind people is increasing with advancement in wireless communication. Artificial intelligent devices with multiple input and output methods are used for reliable data estimation based on maximum probability. A model of a smart home for safe and robust mobility of blind people has been proposed. Fuzzy logic has been used for simulation. Outputs from the internet of things (IoT) devices comprising sensors and bluetooth are taken as input of the fuzzy controller. Rules have been developed based on the conditions and requirements of the blind person to generate decisions as output. These outputs are communicated through IoT devices to assist the blind person or user for safe movement. The proposed system provides the user with easy navigation and obstacle avoidance.
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Wang, Xu. "Research on Methods to Accelerate the Speed of Data Input Based on Library Computer Management System." Applied Mechanics and Materials 687-691 (November 2014): 1724–27. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1724.
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With the development of network technology, communication technology and computer technology, the library circle has raised the climax of building the digital library. In this paper, it is necessary to establish human-machine interactive library management information system based on information technology, computer technology, networking technology to combine information, management with system, and to make library function maximal, service optimal and management canonical. Speed up data entry is one of the core aspects, and rapid data input is the main function of the library management system. After discussing the principles and methods of data input, OPAC systems and bar code technology can satisfy the requirements. Data input and output: the library can easily achieve all business input and output data; data storage and transfer: The database structure is reasonable, rational distribution, and store a variety of data makes the library all business data secure storage and mobile, highly centralized data management and sharing.
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LIN, CHENG-JIAN. "A FUZZY ADAPTIVE LEARNING CONTROL NETWORK WITH ON-LINE STRUCTURE AND PARAMETER LEARNING." International Journal of Neural Systems 07, no. 05 (November 1996): 569–90. http://dx.doi.org/10.1142/s0129065796000567.
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This paper addresses a general connectionist model, called Fuzzy Adaptive Learning Control Network (FALCON), for the realization of a fuzzy logic control system. An on-line supervised structure/parameter learning algorithm is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The supervised learning algorithm has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into “grids”. As the number of input/output variables increase, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the proposed learning algorithm partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the proposed learning algorithm can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. The users thus need not give it any a priori knowledge or even any initial information on these. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a Reinforcement Fuzzy Adaptive Learning Control Network (RFALCON) is further proposed. The proposed RFALCON is constructed by integrating two FALCONs, one FALCON as a critic network, and the other as an action network. By combining temporal difference techniques, stochastic exploration, and a proposed on-line supervised structure/parameter learning algorithm, a reinforcement structure/parameter learning algorithm is proposed, which can construct a RFALCON dynamically through a reward/penalty signal. The ball and beam balancing system is presented to illustrate the performance and applicability of the proposed models and learning algorithms.
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Roj, Jerzy, and Łukasz Dróżdż. "Propagation of Random Errors by the Discrete Wavelet Transform Algorithm." Electronics 10, no. 7 (March 24, 2021): 764. http://dx.doi.org/10.3390/electronics10070764.
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The paper presents considerations concerning the transfer of random errors from the input to the output of the Discrete Wavelet Transform (DWT) algorithm. The concept of determining an uncertainty of its output data based on the probabilistic error description has been presented. The DWT is discussed as the product of the vector of input quantities and the matrix of algorithm coefficients. Calculations of the uncertainty of a single output result of the algorithm are described with assumption that the input quantities are burdened by random errors of known distributions. Theoretical considerations have been verified by simulation experiments using the Monte Carlo method. Determining the uncertainty at the DWT output is possible due to the specific properties of transferring random errors by linear and additive algorithms.
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Hausser, Roland. "Database Semantics." Cadernos de Linguística 2, no. 1 (December 14, 2021): e382. http://dx.doi.org/10.25189/2675-4916.2021.v2.n1.id382.
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For long-term upscaling, the computational reconstruction of a complex natural mechanism must be input-output equivalent with the prototype, i.e. the reconstruction must take the same input and produce the same output in the same processing order as the original. Accordingly, the modeling of natural language communication in Database Semantics (DBS) uses a time-linear derivation order for the speaker’s output and the hearer’s input. The language-dependent surfaces serving as the vehicle of content transfer from speaker to hearer are raw data without meaning or any grammatical properties whatsoever, but measurable by natural science.
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Xu, He, Leixian Shen, Qingyun Zhang, and Guoxu Cao. "Fall Behavior Recognition Based on Deep Learning and Image Processing." International Journal of Mobile Computing and Multimedia Communications 9, no. 4 (October 2018): 1–15. http://dx.doi.org/10.4018/ijmcmc.2018100101.
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Accidental fall detection for the elderly who live alone can minimize the risk of death and injuries. In this article, we present a new fall detection method based on "deep learning and image, where a human body recognition model-DeeperCut is used. First, a camera is used to get the detection source data, and then the video is split into images which can be input into DeeperCut model. The human key point data in the output map and the label of the pictures are used as training data to input into the fall detection neural network. The output model then judges the fall of the subsequent pictures. In addition, the fall detection system is designed and implemented with using Raspberry Pi hardware in a local network environment. The presented method obtains a 100% fall detection rate in the experimental environment. The false positive rate on the test set is around 1.95% which is very low and can be ignored because this will be checked by using SMS, WeChat and other SNS tools to confirm falls. Experimental results show that the proposed fall behavior recognition is effective and feasible to be deployed in home environment.
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Maity, Santigopal, Madutha Santosh Kumar, Chaitali Koley, Debashish Pal, and Ayan Kumar Bandyopadhyay. "Design of the Radio Frequency Section of a Ka-Band Multiple Beam Ladder-Type Extended Interaction Klystron." Electronics 11, no. 22 (November 17, 2022): 3781. http://dx.doi.org/10.3390/electronics11223781.
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Ka-band frequencies are becoming increasingly popular due to their application potential in high-data-rate wireless communications relevant to 5G applications, satellite link establishment, etc. High-power amplifiers in this frequency band, offering several tens of watts of output RF power, are one of the main enabling components of these communication systems. This article reports the design studies and analysis of the radio frequency (RF) section of a multiple beam-extended interaction klystron (MB-EIK). The proposed multiple beam RF section with a ladder-type EIK structure offers several crucial features, such as a low-voltage operation, moderate operational bandwidth, and high output power. Starting from the design of the intermediate cavities, the input and output sections and the overall RF section are presented. The proposed RF section supports the operation at 28.5 GHz center frequency with about a 500 MHz 3 dB bandwidth employing four electron beams with a 4 kV DC accelerating field.
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Buulolo, Novelius, and Anita Sindar. "Analisis dan Perancangan Keamanan Data Teks Menggunakan Algoritma Kriptografi DES (Data Encryption Standard)." Respati 15, no. 3 (November 10, 2020): 61. http://dx.doi.org/10.35842/jtir.v15i3.373.
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INTISARIDalam mengirim pesan ataupun pertukaran informasi menggunakan koneksi internet melalui alat komunikasi, data tersebut bisa dibaca oleh orang lain. Untuk mengatasi masalah ini diperlukan keamanan komputer untuk menjaga data dari pihak yang tidak berwenang. Permasalahan yang muncul adanya data atau dokumen yang tidak aman pada software, keamanan data pada software maka perlu diterapkan algoritma kriptografi DES dalam merancang aplikasi untuk menjaga kerahasiaan data. Data dienkrip dalam blok-blok 64 bit menggunakan kunci 56 bit. DES mentransformasikan input 64 bit dalam beberapa tahap enkripsi ke dalam output 64 bit, dengan tahapan dan kunci yang sama. . Kata kunci— Keamanan Data, Algortima DES, Cipherteks, Plainteks ABSTRACTIn sending messages or exchanging information using an internet connection via communication tools, the data can be read by other people. To solve this problem, computer security is needed to protect data from unauthorized parties. Problems that arise are data or documents that are not secure in the software, data security in the software, it is necessary to apply the DES cryptographic algorithm in designing applications to maintain data confidentiality. Data is encrypted in 64-bit blocks using a 56-bit key. DES transforms 64-bit input in several encryption steps into 64-bit output with the same stages and keys. Keywords— Data Security, DES Algorithm, Ciphertext, Plaintext
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KIM, JONGWAN, JESUNG AHN, and SEONGWON CHO. "ENSEMBLE COMPETITIVE LEARNING NEURAL NETWORKS WITH REDUCED INPUT DIMENSION." International Journal of Neural Systems 06, no. 02 (June 1995): 133–42. http://dx.doi.org/10.1142/s0129065795000111.
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Conventional neural networks utilize all the dimensions of the original input patterns for training and classification. However, a particular attribute of the input patterns does not necessarily contribute to classification and may even cause misclassification in certain cases. A new ensemble competitive learning method using the reduced input dimension is proposed. In contrast to the previous ensemble neural networks which adjust learning parameters, the proposed method takes advantage of the information in each dimension of the input patterns. Since the degree of contribution of each attribute to classification is not known beforehand, the different input data sets with one dimension reduced are presented to multiple neural networks. The classification information from each competitive learning neural network is then combined to make a final decision for classification. In order to improve classification accuracy, the ambiguous output neurons are eliminated which cannot be assigned to any class after training. We use three consensus schemes to judge the classification using ensemble neural networks. The experimental results with remote sensing and speech data indicate the improved performance of the proposed method.
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Wang, Lanmei, Yao Wang, Guibao Wang, and Jianke Jia. "Near-field sound source localization using principal component analysis–multi-output support vector regression." International Journal of Distributed Sensor Networks 16, no. 4 (April 2020): 155014772091640. http://dx.doi.org/10.1177/1550147720916405.
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In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.